Hongin Kim
commited on
Commit
·
cf69e7a
1
Parent(s):
f634d75
Initial model release
Browse files- .gitattributes +1 -0
- README.md +319 -1
- added_tokens.json +28 -0
- chat_template.jinja +120 -0
- config.json +68 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +758 -0
- preprocessor_config.json +39 -0
- special_tokens_map.json +31 -0
- test_sampled_test_current_only_v3.json +0 -0
- tokenizer.json +3 -0
- tokenizer_config.json +241 -0
- trainer_state.json +90 -0
- video_preprocessor_config.json +41 -0
- vocab.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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-
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| 1 |
---
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| 2 |
license: apache-2.0
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| 3 |
+
language:
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+
- en
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| 5 |
+
base_model:
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+
- snuh/mvl-rrg-1.0
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+
tags:
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- radiology report generation
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+
- medical vision
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- clinical
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- benchmark
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- healthcare
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---
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| 14 |
+
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| 15 |
+
🧠 **Temporal & Multi-CXR Chest X-ray Report Generation Model by HARI and MVL of Seoul National University Hospital**
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| 16 |
+
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| 17 |
+
Welcome to the official repository of the **Temporal & Multi-CXR Chest X-ray Report Generation Model** developed by the **Healthcare AI Research Institute (HARI)** and **Medical Vision Lab(MVL)** at **Seoul National University Hospital (SNUH)**.
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| 18 |
+
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+
This model generates chest X-ray (CXR) reports and is designed to leverage not only single-image inputs, but also **multi-view CXRs (PA/AP/Lateral) and temporal pairs (current + prior)**. When available, it can additionally incorporate textual clinical context such as prior reports, indication, and time interval.
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+
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It is trained with instruction data tailored to different input configurations (current only / current + prior / current + prior + prior report), and applies **report style constraints (structure, sentence count, temporal expressions, etc.) to reduce linguistic variation and encourage the model to focus more on clinically meaningful findings and temporal changes.**
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+
## 🚀 Model Overview
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| 25 |
+
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* **Model Name**: `snuh/mvl-rrg-1.0`
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* **Architecture**: Large Multimodal Model (LMM)
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* **Fine-tuning Objective**: Radiology report generation
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* **Primary Language**: English
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* **Domain**: Chest X-ray
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* **Performance**: Achieves state-of-the-art performance on standard report generation benchmarks
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* **Key Applications**:
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* Multi-view CXR inputs (PA/AP/Lateral)
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* Temporal pairs CXR inputs (current + prior)
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* Style-controlled report generation to reduce linguistic variance
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+
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## 📊 Training Data & Benchmark
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This model was fine-tuned using a curated corpus of medical report generation data derived from **publicly available, de-identified sources**, including **MIMIC-CXR** and **MIMIC-CXR reports**. The training data focuses on radiology report generation from chest X-ray images.
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+
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* **Training Data Characteristics**:
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| 43 |
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- Focused on generating radiology reports from chest X-ray images.
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- Utilizes chest X-ray images and corresponding radiology reports from the MIMIC-CXR dataset.
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- Incorporates longitudinal imaging data with two or more time points, enabling the model to understand sequential changes in patient conditions.
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- Designed to reflect realistic radiological interpretation and documentation workflows.
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- The current dataset consists of 80,136 training samples and 665 test samples, ensuring robust model training and evaluation.
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- Samples in which the radiology report referenced a prior examination but no corresponding prior data could be mapped were excluded from the dataset.
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### Evaluation Scope and Benchmark Results
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The reported benchmark results focus on **current-only report generation**, where each report is generated using a single, self-contained imaging context without explicit temporal inputs.
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| 53 |
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In medical imaging, this setting differs fundamentally from **temporal (longitudinal) report generation**, which requires reasoning over disease progression, treatment response, or follow-up changes. Temporal information can substantially alter clinical interpretation, even when surface-level imaging findings appear similar.
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+
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Accordingly, we distinguish between the following evaluation regimes:
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- **Current-only evaluation**
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Single-image, single-context report generation.
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All reported benchmark results are based on this setting.
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| Model | ROUGE-L | BLEU-1 | BLEU-4 | RadGraph F1 | RadCliQ (↓) |
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|:------------|--------:|-------:|-------:|------------:|------------:|
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| Libra | 25.6 | 33.0 | 9.1 | 24.5 | 0.92 |
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| MAIRA-2 | 29.9 | 44.7 | 14.9 | 34.7 | 1.27 |
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| mvl-rrg-1.0 | 34.1 | 44.6 | 18.6 | 34.9 | 1.23 |
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- **Temporal evaluation (ongoing)**
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Time-aware report generation that incorporates prior imaging studies and longitudinal clinical changes.
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| Model | Temporal RadGraph F1 |
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|:------------|---------------------:|
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| Libra | 54.8 |
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| MAIRA-2 | 52.5 |
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| mvl-rrg-1.0 | 79.9 |
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> ⚠️ These benchmarks are provided for research purposes only and do not imply clinical safety or efficacy.
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## 🔐 Privacy & Ethical Compliance
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We strictly adhere to ethical AI development and privacy protection:
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+
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* ✅ The model was trained exclusively on **publicly available and de-identified data**.
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* 🔒 It does **not include any real patient data or personally identifiable information (PII)**.
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* ⚖️ Designed for **safe, responsible, and research-oriented** use in healthcare AI.
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> ⚠️ This model is intended for **research and educational purposes only** and should **not** be used to make clinical decisions.
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## 🏥 About HARI and MVL of Seoul National University Hospital
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HARI – Healthcare AI Research Institute
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The **Healthcare AI Research Institute (HARI)** is a pioneering research group within **Seoul National University Hospital**, driving innovation in medical AI.
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MVL - Medical Vison Lab
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The **Medical Vison Lab (MVL)** is a pioneering research group within **Seoul National University Hospital**, driving innovation in medical AI.
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* To develop AI technology-based applications that will aid doctors in fast and accurate diagnostic decisions helping patients have a comfortable life and eventually improve their life quality.
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### 🌍 Vision & Mission
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* **Vision**: Shaping a sustainable and healthy future through pioneering AI research.
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* **Mission**:
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* Develop clinically useful, trustworthy AI technologies.
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* Foster cross-disciplinary collaboration in medicine and AI.
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* Lead global healthcare AI commercialization and policy frameworks.
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* Educate the next generation of AI-powered medical professionals.
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## 🤝 Collaborate with Us
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We welcome collaboration with:
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* AI research institutions and medical universities
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* Healthcare startups and technology partners
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* Policymakers shaping AI regulation in medicine
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* HARI
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| 117 |
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📧 **Contact**: [hhoon@snu.ac.kr](mailto:hhoon@snu.ac.kr)
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🌐 **Website**: [Seoul National University Hospital](https://www.snuh.org/)
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| 119 |
+
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* MVL
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📧 **Contact**: [yg@snuh.org](mailto:yg@snuh.org)
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| 123 |
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🌐 **Website**: [Medical Vison Lab](https://sites.google.com/view/MedicalVisionLab)
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## 🤗 Model Usage Example
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+
```python
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| 129 |
+
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
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| 130 |
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import torch
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| 131 |
+
from pathlib import Path
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| 132 |
+
import os
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| 133 |
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from PIL import Image
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| 134 |
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| 135 |
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# Load processor and model
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| 136 |
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model_name = "Qwen3VL_SNUH"
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+
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| 138 |
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model = Qwen3VLForConditionalGeneration.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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| 141 |
+
device_map="auto"
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| 142 |
+
)
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processor = AutoProcessor.from_pretrained(model_name)
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+
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# Image paths
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| 146 |
+
current_frontal_image_path = "/**/current_frontal_image.png"
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current_lateral_image_path = "/**/current_lateral_image.png"
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prior_frontal_image_path = "/**/prior_frontal_image.png"
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| 149 |
+
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| 150 |
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# Validate image paths exist
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| 151 |
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if current_frontal_image_path and not Path(current_frontal_image_path).exists():
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| 152 |
+
raise FileNotFoundError(f"Current frontal image file not found: {current_frontal_image_path}")
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| 153 |
+
if current_lateral_image_path and not Path(current_lateral_image_path).exists():
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| 154 |
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raise FileNotFoundError(f"Current lateral image file not found: {current_lateral_image_path}")
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| 155 |
+
if prior_frontal_image_path and not Path(prior_frontal_image_path).exists():
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raise FileNotFoundError(f"Prior frontal image file not found: {prior_frontal_image_path}")
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+
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# Clinical context
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prior_findings = "N/A"
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prior_impression = "Developed pleural effusion, both\nInterval increased nodular opacity at LMLF"
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indication = "F with chest pain // ?pna"
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technique = "CHEST (PA AND LAT)"
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comparison = "__."
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time_interval = "1 month"
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+
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# Style attributes
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findings_structure_type = "narrative_paragraph"
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| 168 |
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findings_temporal_comparison = "absent"
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findings_sentence_count = 6
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impression_structure_type = "narrative_paragraph"
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| 171 |
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impression_temporal_comparison = "absent"
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impression_sentence_count = 1
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| 173 |
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# Instruction
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| 175 |
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inputs_list = ["- Current frontal image: <image>"]
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| 176 |
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if current_lateral_image_path:
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inputs_list.append("- Current lateral image: <image>")
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else:
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inputs_list.append("- Current lateral image: N/A")
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if prior_frontal_image_path:
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| 181 |
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inputs_list.append("- Prior frontal image: <image>")
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| 182 |
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else:
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| 183 |
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inputs_list.append("- Prior frontal image: N/A")
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inputs_list.extend([
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| 185 |
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f"- Prior findings: {prior_findings}",
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| 186 |
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f"- Prior impression: {prior_impression}"
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])
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| 188 |
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inputs_text = "\n".join(inputs_list)
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| 189 |
+
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| 190 |
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instruction = f"""You are an expert radiology assistant for chest X-ray (CXR) interpretation.
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Inputs:
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| 193 |
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{inputs_text}
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| 194 |
+
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Clinical context:
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| 196 |
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- INDICATION: {indication}
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| 197 |
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- TECHNIQUE: {technique}
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| 198 |
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- COMPARISON: {comparison}
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| 199 |
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- TIME INTERVAL: {time_interval}
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(Time elapsed between the prior study date and the current study date)
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Instructions:
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| 203 |
+
1. Generate a chest X-ray report based on the current study.
|
| 204 |
+
2. Write a Findings section describing radiographic observations using standard clinical language.
|
| 205 |
+
3. Write an Impression section summarizing the key findings or overall assessment.
|
| 206 |
+
4. When applicable, include conditions related to CheXbert classes
|
| 207 |
+
(e.g., cardiomegaly, lung opacity, pleural effusion, pneumothorax, pneumonia,
|
| 208 |
+
support devices, or no acute abnormality).
|
| 209 |
+
5. If no significant abnormality is present, clearly state this.
|
| 210 |
+
6. Follow the provided style attributes exactly, applying them independently
|
| 211 |
+
to the Findings and Impression sections:
|
| 212 |
+
- Structure type controls the organizational pattern of the text.
|
| 213 |
+
- Temporal comparison controls whether and how prior studies are referenced.
|
| 214 |
+
- Sentence count controls the amount of text (small / medium / large).
|
| 215 |
+
|
| 216 |
+
Output format:
|
| 217 |
+
Return only a single JSON object with the following fields:
|
| 218 |
+
|
| 219 |
+
{{
|
| 220 |
+
"findings": "<free-text radiology findings>",
|
| 221 |
+
"impression": "<free-text radiology impression>"
|
| 222 |
+
}}
|
| 223 |
+
|
| 224 |
+
Style attributes:
|
| 225 |
+
- findings_structure_type: {findings_structure_type}
|
| 226 |
+
- findings_temporal_comparison: {findings_temporal_comparison}
|
| 227 |
+
- findings_sentence_count: {findings_sentence_count}
|
| 228 |
+
- impression_structure_type: {impression_structure_type}
|
| 229 |
+
- impression_temporal_comparison: {impression_temporal_comparison}
|
| 230 |
+
- impression_sentence_count: {impression_sentence_count}"""
|
| 231 |
+
|
| 232 |
+
content = []
|
| 233 |
+
|
| 234 |
+
# Current frontal image (always required)
|
| 235 |
+
current_frontal_image = Image.open(current_frontal_image_path)
|
| 236 |
+
content.append({
|
| 237 |
+
"type": "images",
|
| 238 |
+
"image": current_frontal_image,
|
| 239 |
+
})
|
| 240 |
+
|
| 241 |
+
# Current lateral image (optional)
|
| 242 |
+
if current_lateral_image_path:
|
| 243 |
+
current_lateral_image = Image.open(current_lateral_image_path)
|
| 244 |
+
content.append({
|
| 245 |
+
"type": "images",
|
| 246 |
+
"image": current_lateral_image,
|
| 247 |
+
})
|
| 248 |
+
|
| 249 |
+
# Prior frontal image (optional)
|
| 250 |
+
if prior_frontal_image_path:
|
| 251 |
+
prior_frontal_image = Image.open(prior_frontal_image_path)
|
| 252 |
+
content.append({
|
| 253 |
+
"type": "images",
|
| 254 |
+
"image": prior_frontal_image,
|
| 255 |
+
})
|
| 256 |
+
|
| 257 |
+
# Instruction
|
| 258 |
+
content.append({
|
| 259 |
+
"type": "text",
|
| 260 |
+
"text": instruction,
|
| 261 |
+
})
|
| 262 |
+
|
| 263 |
+
messages = [
|
| 264 |
+
{
|
| 265 |
+
"role": "user",
|
| 266 |
+
"content": content,
|
| 267 |
+
}
|
| 268 |
+
]
|
| 269 |
+
|
| 270 |
+
inputs = processor.apply_chat_template(
|
| 271 |
+
messages,
|
| 272 |
+
tokenize=True,
|
| 273 |
+
add_generation_prompt=True,
|
| 274 |
+
return_tensors="pt",
|
| 275 |
+
return_dict=True,
|
| 276 |
+
)
|
| 277 |
+
|
| 278 |
+
inputs = {k: v.to(model.device) for k, v in inputs.items()}
|
| 279 |
+
|
| 280 |
+
with torch.no_grad():
|
| 281 |
+
generated_ids = model.generate(
|
| 282 |
+
**inputs,
|
| 283 |
+
max_new_tokens=512
|
| 284 |
+
)
|
| 285 |
+
|
| 286 |
+
prompt_len = inputs["input_ids"].shape[-1]
|
| 287 |
+
generated_ids_trimmed = generated_ids[:, prompt_len:]
|
| 288 |
+
|
| 289 |
+
response = processor.batch_decode(
|
| 290 |
+
generated_ids_trimmed,
|
| 291 |
+
skip_special_tokens=True,
|
| 292 |
+
clean_up_tokenization_spaces=False,
|
| 293 |
+
)[0]
|
| 294 |
+
|
| 295 |
+
#result
|
| 296 |
+
print(response)
|
| 297 |
+
````
|
| 298 |
+
|
| 299 |
+
|
| 300 |
+
## 📄 License
|
| 301 |
+
|
| 302 |
+
**Apache 2.0 License** – Free for research and commercial use with attribution.
|
| 303 |
+
|
| 304 |
+
|
| 305 |
+
## 📢 Citation
|
| 306 |
+
|
| 307 |
+
If you use this model in your work, please cite:
|
| 308 |
+
|
| 309 |
+
```
|
| 310 |
+
@misc{mvl-rrg-1.0,
|
| 311 |
+
title = {mvl-rrg-1.0},
|
| 312 |
+
url = {https://huggingface.co/snuh/mvl-rrg-1.0},
|
| 313 |
+
author = {Healthcare AI Research Institute(HARI) and Medical Vison Lab (MVL) of Seoul National University Hospital(SNUH)},
|
| 314 |
+
month = {January},
|
| 315 |
+
year = {2026}
|
| 316 |
+
}
|
| 317 |
+
```
|
| 318 |
+
|
| 319 |
+
|
| 320 |
+
## 🚀 Together, we are shaping the future of AI-driven healthcare.
|
| 321 |
+
|
added_tokens.json
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"</think>": 151668,
|
| 3 |
+
"</tool_call>": 151658,
|
| 4 |
+
"</tool_response>": 151666,
|
| 5 |
+
"<think>": 151667,
|
| 6 |
+
"<tool_call>": 151657,
|
| 7 |
+
"<tool_response>": 151665,
|
| 8 |
+
"<|box_end|>": 151649,
|
| 9 |
+
"<|box_start|>": 151648,
|
| 10 |
+
"<|endoftext|>": 151643,
|
| 11 |
+
"<|file_sep|>": 151664,
|
| 12 |
+
"<|fim_middle|>": 151660,
|
| 13 |
+
"<|fim_pad|>": 151662,
|
| 14 |
+
"<|fim_prefix|>": 151659,
|
| 15 |
+
"<|fim_suffix|>": 151661,
|
| 16 |
+
"<|im_end|>": 151645,
|
| 17 |
+
"<|im_start|>": 151644,
|
| 18 |
+
"<|image_pad|>": 151655,
|
| 19 |
+
"<|object_ref_end|>": 151647,
|
| 20 |
+
"<|object_ref_start|>": 151646,
|
| 21 |
+
"<|quad_end|>": 151651,
|
| 22 |
+
"<|quad_start|>": 151650,
|
| 23 |
+
"<|repo_name|>": 151663,
|
| 24 |
+
"<|video_pad|>": 151656,
|
| 25 |
+
"<|vision_end|>": 151653,
|
| 26 |
+
"<|vision_pad|>": 151654,
|
| 27 |
+
"<|vision_start|>": 151652
|
| 28 |
+
}
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,120 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- if tools %}
|
| 2 |
+
{{- '<|im_start|>system\n' }}
|
| 3 |
+
{%- if messages[0].role == 'system' %}
|
| 4 |
+
{%- if messages[0].content is string %}
|
| 5 |
+
{{- messages[0].content }}
|
| 6 |
+
{%- else %}
|
| 7 |
+
{%- for content in messages[0].content %}
|
| 8 |
+
{%- if 'text' in content %}
|
| 9 |
+
{{- content.text }}
|
| 10 |
+
{%- endif %}
|
| 11 |
+
{%- endfor %}
|
| 12 |
+
{%- endif %}
|
| 13 |
+
{{- '\n\n' }}
|
| 14 |
+
{%- endif %}
|
| 15 |
+
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 16 |
+
{%- for tool in tools %}
|
| 17 |
+
{{- "\n" }}
|
| 18 |
+
{{- tool | tojson }}
|
| 19 |
+
{%- endfor %}
|
| 20 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 21 |
+
{%- else %}
|
| 22 |
+
{%- if messages[0].role == 'system' %}
|
| 23 |
+
{{- '<|im_start|>system\n' }}
|
| 24 |
+
{%- if messages[0].content is string %}
|
| 25 |
+
{{- messages[0].content }}
|
| 26 |
+
{%- else %}
|
| 27 |
+
{%- for content in messages[0].content %}
|
| 28 |
+
{%- if 'text' in content %}
|
| 29 |
+
{{- content.text }}
|
| 30 |
+
{%- endif %}
|
| 31 |
+
{%- endfor %}
|
| 32 |
+
{%- endif %}
|
| 33 |
+
{{- '<|im_end|>\n' }}
|
| 34 |
+
{%- endif %}
|
| 35 |
+
{%- endif %}
|
| 36 |
+
{%- set image_count = namespace(value=0) %}
|
| 37 |
+
{%- set video_count = namespace(value=0) %}
|
| 38 |
+
{%- for message in messages %}
|
| 39 |
+
{%- if message.role == "user" %}
|
| 40 |
+
{{- '<|im_start|>' + message.role + '\n' }}
|
| 41 |
+
{%- if message.content is string %}
|
| 42 |
+
{{- message.content }}
|
| 43 |
+
{%- else %}
|
| 44 |
+
{%- for content in message.content %}
|
| 45 |
+
{%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
|
| 46 |
+
{%- set image_count.value = image_count.value + 1 %}
|
| 47 |
+
{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
|
| 48 |
+
<|vision_start|><|image_pad|><|vision_end|>
|
| 49 |
+
{%- elif content.type == 'video' or 'video' in content %}
|
| 50 |
+
{%- set video_count.value = video_count.value + 1 %}
|
| 51 |
+
{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
|
| 52 |
+
<|vision_start|><|video_pad|><|vision_end|>
|
| 53 |
+
{%- elif 'text' in content %}
|
| 54 |
+
{{- content.text }}
|
| 55 |
+
{%- endif %}
|
| 56 |
+
{%- endfor %}
|
| 57 |
+
{%- endif %}
|
| 58 |
+
{{- '<|im_end|>\n' }}
|
| 59 |
+
{%- elif message.role == "assistant" %}
|
| 60 |
+
{{- '<|im_start|>' + message.role + '\n' }}
|
| 61 |
+
{%- if message.content is string %}
|
| 62 |
+
{{- message.content }}
|
| 63 |
+
{%- else %}
|
| 64 |
+
{%- for content_item in message.content %}
|
| 65 |
+
{%- if 'text' in content_item %}
|
| 66 |
+
{{- content_item.text }}
|
| 67 |
+
{%- endif %}
|
| 68 |
+
{%- endfor %}
|
| 69 |
+
{%- endif %}
|
| 70 |
+
{%- if message.tool_calls %}
|
| 71 |
+
{%- for tool_call in message.tool_calls %}
|
| 72 |
+
{%- if (loop.first and message.content) or (not loop.first) %}
|
| 73 |
+
{{- '\n' }}
|
| 74 |
+
{%- endif %}
|
| 75 |
+
{%- if tool_call.function %}
|
| 76 |
+
{%- set tool_call = tool_call.function %}
|
| 77 |
+
{%- endif %}
|
| 78 |
+
{{- '<tool_call>\n{"name": "' }}
|
| 79 |
+
{{- tool_call.name }}
|
| 80 |
+
{{- '", "arguments": ' }}
|
| 81 |
+
{%- if tool_call.arguments is string %}
|
| 82 |
+
{{- tool_call.arguments }}
|
| 83 |
+
{%- else %}
|
| 84 |
+
{{- tool_call.arguments | tojson }}
|
| 85 |
+
{%- endif %}
|
| 86 |
+
{{- '}\n</tool_call>' }}
|
| 87 |
+
{%- endfor %}
|
| 88 |
+
{%- endif %}
|
| 89 |
+
{{- '<|im_end|>\n' }}
|
| 90 |
+
{%- elif message.role == "tool" %}
|
| 91 |
+
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
| 92 |
+
{{- '<|im_start|>user' }}
|
| 93 |
+
{%- endif %}
|
| 94 |
+
{{- '\n<tool_response>\n' }}
|
| 95 |
+
{%- if message.content is string %}
|
| 96 |
+
{{- message.content }}
|
| 97 |
+
{%- else %}
|
| 98 |
+
{%- for content in message.content %}
|
| 99 |
+
{%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
|
| 100 |
+
{%- set image_count.value = image_count.value + 1 %}
|
| 101 |
+
{%- if add_vision_id %}Picture {{ image_count.value }}: {% endif -%}
|
| 102 |
+
<|vision_start|><|image_pad|><|vision_end|>
|
| 103 |
+
{%- elif content.type == 'video' or 'video' in content %}
|
| 104 |
+
{%- set video_count.value = video_count.value + 1 %}
|
| 105 |
+
{%- if add_vision_id %}Video {{ video_count.value }}: {% endif -%}
|
| 106 |
+
<|vision_start|><|video_pad|><|vision_end|>
|
| 107 |
+
{%- elif 'text' in content %}
|
| 108 |
+
{{- content.text }}
|
| 109 |
+
{%- endif %}
|
| 110 |
+
{%- endfor %}
|
| 111 |
+
{%- endif %}
|
| 112 |
+
{{- '\n</tool_response>' }}
|
| 113 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 114 |
+
{{- '<|im_end|>\n' }}
|
| 115 |
+
{%- endif %}
|
| 116 |
+
{%- endif %}
|
| 117 |
+
{%- endfor %}
|
| 118 |
+
{%- if add_generation_prompt %}
|
| 119 |
+
{{- '<|im_start|>assistant\n' }}
|
| 120 |
+
{%- endif %}
|
config.json
ADDED
|
@@ -0,0 +1,68 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Qwen3VLForConditionalGeneration"
|
| 4 |
+
],
|
| 5 |
+
"dtype": "bfloat16",
|
| 6 |
+
"eos_token_id": 151645,
|
| 7 |
+
"hidden_size": 4096,
|
| 8 |
+
"image_token_id": 151655,
|
| 9 |
+
"model_type": "qwen3_vl",
|
| 10 |
+
"pad_token_id": 151643,
|
| 11 |
+
"text_config": {
|
| 12 |
+
"attention_bias": false,
|
| 13 |
+
"attention_dropout": 0.0,
|
| 14 |
+
"bos_token_id": 151643,
|
| 15 |
+
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| 16 |
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| 17 |
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| 18 |
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| 19 |
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| 20 |
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| 21 |
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|
| 22 |
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| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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"rope_scaling": {
|
| 29 |
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|
| 30 |
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"mrope_section": [
|
| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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"rope_type": "default"
|
| 36 |
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| 37 |
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|
| 38 |
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|
| 39 |
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| 40 |
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|
| 42 |
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|
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"vision_config": {
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| 46 |
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"deepstack_visual_indexes": [
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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|
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|
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}
|
generation_config.json
ADDED
|
@@ -0,0 +1,13 @@
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| 4 |
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|
| 5 |
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|
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| 7 |
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| 8 |
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|
| 9 |
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|
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
merges.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
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model.safetensors.index.json
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preprocessor_config.json
ADDED
|
@@ -0,0 +1,39 @@
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|
| 1 |
+
{
|
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|
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"data_format": "channels_first",
|
| 4 |
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|
| 5 |
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|
| 6 |
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|
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|
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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| 14 |
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|
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|
| 17 |
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],
|
| 18 |
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"image_processor_type": "Qwen2VLImageProcessorFast",
|
| 19 |
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| 20 |
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| 21 |
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| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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"patch_size": 16,
|
| 30 |
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"processor_class": "Qwen3VLProcessor",
|
| 31 |
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|
| 32 |
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"rescale_factor": 0.00392156862745098,
|
| 33 |
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| 34 |
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"size": {
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| 36 |
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|
| 37 |
+
},
|
| 38 |
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"temporal_patch_size": 2
|
| 39 |
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}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
|
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|
|
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|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
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|
| 5 |
+
"<|object_ref_start|>",
|
| 6 |
+
"<|object_ref_end|>",
|
| 7 |
+
"<|box_start|>",
|
| 8 |
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"<|box_end|>",
|
| 9 |
+
"<|quad_start|>",
|
| 10 |
+
"<|quad_end|>",
|
| 11 |
+
"<|vision_start|>",
|
| 12 |
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"<|vision_end|>",
|
| 13 |
+
"<|vision_pad|>",
|
| 14 |
+
"<|image_pad|>",
|
| 15 |
+
"<|video_pad|>"
|
| 16 |
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],
|
| 17 |
+
"eos_token": {
|
| 18 |
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"content": "<|im_end|>",
|
| 19 |
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"lstrip": false,
|
| 20 |
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"normalized": false,
|
| 21 |
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"rstrip": false,
|
| 22 |
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"single_word": false
|
| 23 |
+
},
|
| 24 |
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"pad_token": {
|
| 25 |
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"content": "<|endoftext|>",
|
| 26 |
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"lstrip": false,
|
| 27 |
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"normalized": false,
|
| 28 |
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"rstrip": false,
|
| 29 |
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"single_word": false
|
| 30 |
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}
|
| 31 |
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|
test_sampled_test_current_only_v3.json
ADDED
|
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|
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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| 3 |
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size 11422654
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tokenizer_config.json
ADDED
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@@ -0,0 +1,241 @@
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|
| 1 |
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| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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| 7 |
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|
| 8 |
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|
| 9 |
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| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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| 21 |
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|
| 22 |
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|
| 23 |
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| 24 |
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| 25 |
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|
| 26 |
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|
| 27 |
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|
| 28 |
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| 29 |
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|
| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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|
| 35 |
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|
| 36 |
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| 37 |
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|
| 38 |
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| 39 |
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| 42 |
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| 43 |
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|
| 44 |
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| 45 |
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| 46 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 57 |
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| 58 |
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| 60 |
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| 67 |
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| 78 |
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| 86 |
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trainer_state.json
ADDED
|
@@ -0,0 +1,90 @@
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video_preprocessor_config.json
ADDED
|
@@ -0,0 +1,41 @@
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|
| 1 |
+
{
|
| 2 |
+
"crop_size": null,
|
| 3 |
+
"data_format": "channels_first",
|
| 4 |
+
"default_to_square": true,
|
| 5 |
+
"device": null,
|
| 6 |
+
"do_center_crop": null,
|
| 7 |
+
"do_convert_rgb": true,
|
| 8 |
+
"do_normalize": true,
|
| 9 |
+
"do_rescale": true,
|
| 10 |
+
"do_resize": true,
|
| 11 |
+
"do_sample_frames": true,
|
| 12 |
+
"fps": 2,
|
| 13 |
+
"image_mean": [
|
| 14 |
+
0.5,
|
| 15 |
+
0.5,
|
| 16 |
+
0.5
|
| 17 |
+
],
|
| 18 |
+
"image_std": [
|
| 19 |
+
0.5,
|
| 20 |
+
0.5,
|
| 21 |
+
0.5
|
| 22 |
+
],
|
| 23 |
+
"input_data_format": null,
|
| 24 |
+
"max_frames": 768,
|
| 25 |
+
"merge_size": 2,
|
| 26 |
+
"min_frames": 4,
|
| 27 |
+
"num_frames": null,
|
| 28 |
+
"pad_size": null,
|
| 29 |
+
"patch_size": 16,
|
| 30 |
+
"processor_class": "Qwen3VLProcessor",
|
| 31 |
+
"resample": 3,
|
| 32 |
+
"rescale_factor": 0.00392156862745098,
|
| 33 |
+
"return_metadata": false,
|
| 34 |
+
"size": {
|
| 35 |
+
"longest_edge": 1048576,
|
| 36 |
+
"shortest_edge": 4096
|
| 37 |
+
},
|
| 38 |
+
"temporal_patch_size": 2,
|
| 39 |
+
"video_metadata": null,
|
| 40 |
+
"video_processor_type": "Qwen3VLVideoProcessor"
|
| 41 |
+
}
|
vocab.json
ADDED
|
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|
|
|